The Research Of Monitoring And Assessment Technology On Flood Disaster Of Maize Based On Remote Sensing Data

With the change of global climate, flood disaster has become one of the important types of disasters. Flood disaster will have a significant impact on agricultural production, which directly restricts the development of the national economy. Scientific and rapid monitoring of flood disaster is of great significance to guide agricultural production, and it is also helpful to carry out insurance compensation and disaster compensation. At present, research on flood disaster mainly concentrated in aspects of population security, infrastructure and property losses, according to crop and flood disaster monitoring, early warning and assessment technology research is rare reported. This is not only because of crop planting area wide, reproductive cycle is long, and considering many factors of crop varieties, cultivated land water and fertilizer conditions, growth stages and soil texture. Thus, the crops, especially corn flood disaster monitoring and loss evaluation has not formed a relatively perfect foundation, theory and practice of research results.In this paper, based on the Shandong Province agriculture major application technology innovation subject task "The research of monitoring, warning and assessment technology on flood disaster of maize based on remote sensing data ". Through the ground positioning observation and artificial simulation experiment, the establishment of remote sensing monitoring method and different period, different levels of corn flood disaster evaluation system. Promote the early warning, monitoring and evaluation of agricultural disasters in Shandong Province, and promote the process of agricultural information.In this paper, the floods of corn as the research object, through the cell simulation test, starting from the time, factors affect the growth and development of maize growth period of waterlogging, corn leaf area index(LAI) in different growth periods and different degree of Flood Stress, changes of chlorophyll content and yield loss, through the corn optical spectral data acquisition, biological parameters acquisition, spectral feature extraction and location of vegetation index construction are analyzed by means of waterlogging stress on Maize spectral characteristics, and combined with the correlation analysis method to construct inversion factors of corn leaf area index, chlorophyll content and yield and yield estimation models, provides a theoretical basis for remote sensing monitoring from maize flood growth conditions. In addition, using Landsat satellite data and analysis of extracted in Liaocheng City of Shandong Province as the representative of the main region of corn in the corn planting area, and combined with the local actual flood disaster, through remote sensing images to the flood disaster area identification, extraction effect with the civil affairs departments statistics is consistent with the data, to illustrate the feasibility of flood disaster in the corn of remote sensing monitoring, provides the prior condition of subsequent research work. In addition, flood warning mechanism of corn has been investigated, mainly based on influencing factors, such as geological conditions, soil type, hydrology data, road information, DEM and meteorological data. The analysis of the warning model, for the development of early warning system in the future, theoretically provides feasible basis.